DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the current action filed on 08/06/2025.
Claims 1 and 20 have been amended.
Claims 1-21 are currently pending and have been examined.
This action is made FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are rejected under 35 U.S.C. § 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 analysis:
Independent Claims 1 and 20 are directed to a method and a non-transitory computer-readable medium (i.e. machine) respectively and therefore fall into one of the four statutory categories. Dependent Claims 2-19 are additionally directed to a method and Claim 21 to a non-transitory computer-readable medium, and therefore the dependent claims also fall into one of the four statutory categories.
Step 2A analysis- prong one:
Claim 1, which is representative of the inventive concept, recites the following:
A method of designing a hybrid clinical trial including an external control arm (ECA) study located in at least one site, to support a randomized clinical trial (RCT) study for a treatment of a medical condition, the method comprising:
administering the treatment to at least some of a plurality of RCT participants, wherein each RCT participant is an individual person;
detecting at least one outlier ECA candidate in a plurality of candidate records in an ECA candidate database located in the at least one site of a plurality of ECA sites, wherein each candidate record corresponds to an individual person receiving a standard of care (SOC) treatment for the condition, by calculating a Mahalanobis distance value based on:
a point comprising a set of values corresponding to a first plurality of feature variables obtained from the candidate record corresponding to the at least one outlier ECA candidate,
wherein each candidate record corresponds to an ECA candidate and comprises information about administering the SOC treatment to the ECA candidate;
and a distribution comprising a plurality of points, wherein each point comprises a set of values corresponding to the first plurality of feature variables obtained from each of a plurality of RCT participant records in a RCT participant database,
wherein each participant record corresponds to an RCT participant and comprises information about administering the treatment to the RCT participant;
excluding at least one outlier ECA candidate from at least one ECA participant database at the at least one site based on whether the Mahalanobis distance value meets a specified criteria;
dynamically adjusting recruitment into at least one ECA participant database at the at least one site that is recruiting participants into the ECA study by comparing a set of values corresponding to a second set of feature variables obtained from the ECA participant records in at least one ECA participant database to a set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database wherein the RCT feature is compared to a pooled set of ECA sites so that balance is achieved between the RCT and the set of ECA sites pooled together;
and matching propensity scores for exposed and unexposed individuals in the RCT participant databases and the set of ECA sites to obtain an unbiased estimate of an average causal effect of the treatment on the medica condition.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions- in this case, following steps to obtain information, determine outliers, adjust recruitment, and match propensity scores to determine an estimate of average causal effective of a treatment), e.g. see MPEP 2106.04(a)(2)(II). Any limitations not identified as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2, 4-9, 11, 14-19 recite additional limitations directed to the abstract idea. For example, Claim 2 recites calculating propensity scores, Claim 4 recites matching ECA participants to RCT participants, Claim 5 recites the propensity scores are used to weight ECA and RCT participants, Claim 6 recites weighting the propensity score downward or upward based on similarity of the records in the database, Claim 7 recites the propensity score is a real number, Claim 8 recites patient data being weighted in accordance with an overlap weighting methodology, Claim 9 recites the patient data are weighted in accordance with an inverse probability or treatment weighing (IPTW) methodology, Claim 11 recites the use of EHR and non-EHR data, Claim 14 recites adjusting recruitment when an imbalance is identified, Claim 15 recites dynamically adjusting recruitment at periodic time intervals, Claim 16 recites the periodic time interval is at least monthly, Claim 17 recites calculating an absolute standardized mean difference between at least one feature variable of the RCT and ECA participant records and identifying imbalances, Claim 18 recites the threshold value is at least 0.10, Claim 19 recites contacting at least one site and adding one or more ECA candidate records. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent Claims 2, 4-9, 11, 14-19 are nonetheless directed toward fundamentally the same abstract idea as independent Claims 1 and 20.
Step 2A analysis- prong two:
Claims 1 and 20 are not integrated into practical application because the additional elements (i.e. the non-underlined portions presented in prong one- in this case, the ECA candidate database, ECA participant database, and the RCT participant database of Claim 1, and the ECA candidate database, ECA participant database, RCT participant database, and non-transitory computer-readable medium of Claim 20) are recited at a high level of generality (i.e. as a generic processor performing generic computer function) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, applicant’s specification states dynamically adjusting recruitment from at least one site recruiting participants into the ECA comprises adding one or more ECA candidate records from at least one site-specific ECA candidate database to at least one ECA participant database when an imbalance is identified [0064]. A method…comprising…dynamically adjusting recruitment into at least one ECA participant database at the at least one site that is recruiting participants into the ECA study by comparing a set of values corresponding to a second set of feature variables obtained from the ECA participant records in at least one ECA participant database to a set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database [0051]. The electronically readable medium may be any non-transitory medium that stores information electronically and may be access locally or remotely, for example via a network connection [0044]. A processor such as processor 520 may be implemented as one or more general purpose processors (preferably having multiple cores) without necessarily departing from the spirit and scope of the present invention [0049]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, Claims 1 and 20 are directed to an abstract idea without practical application.
Dependent Claims 2-6, 8-14, 17, and 21 recite additional elements, but these limitations amount to no more than mere instructions to apply an exception. Claim 2 recites a new additional element of a propensity score model and specifies the propensity score model is fitted by calculating propensity scores, Claim 3 recites new additional elements of a logistic regression model, machine learning based propensity score model, probit model, neural networks, support vector machines, decision trees, or meta-classifiers. Claim 4 recites the previously recited propensity score model and specifies the propensity scores are estimated with the propensity score model and used to match ECA participants to RCT participants. Claim 5 recites the previously recited propensity score model and specifies the propensity scores are estimated by the propensity score model and the scores are used to weight the ECA participants and RCT participants. Claim 6 recites the previously taught propensity score model, RCT participant database, and ECA participant database. Claims 8 and 9 repeat the previously recited ECA and RCT participant databases and specify the participant databased are weighted by the propensity score with an overlap weighting methodology and an IPTW methodology, respectively. Claim 10 recites an ECA and EHR database and specifies the ECA candidate database comprises an EHR database. Claim 11 recites a previously recited ECA candidate database and specifies the database comprises both EHR and non-EHR data. Claim 12 introduces a clinical database and specifies the non-EHR data comprises a clinical database at a site. Claim 13 introduces a Patient Reported Outcomes database and specifies the non-EHR data comprises a PROs database. Claim 14 recites the previously recited additional elements of ECA candidate database, ECA participant database, and RCT participant database and specifies adding ECA candidate records from a site-specific ECA database to a ECA participant database when an imbalance is identified. Claim 17 recites the previously recited additional elements of ECA candidate database, ECA participant database, and RCT participant database and specifies calculating an absolute standardized mean difference (aSMD) metric between a variable of the RCT participant records in the RCT participant database and the aSMD metric for at least one feature variable of the ECA participant databases; and identifying an imbalance when the aSMD metric between at least one feature variable of the RCT participant records in the RCT participant. Claim 19 recites the previously recited additional elements of ECA candidate database, ECA participant database, and RCT participant database and specifies contacting a site when the aSMD metric between the RCT participant records in the RCT participant database and the ECA participant records at the site indicates an imbalance; and adding ECA candidate records from the ECA candidate database at the at least one site into the at least one ECA participant database. Claim 21 recites a new additional element of processors. However, these additional elements are recited only at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the memory and processor of Claim 1, and the ECA candidate database, ECA participant database, and the RCT participant database of Claim 1, and the ECA candidate database, ECA participant database, and the RCT participant database, and non-transitory computer-readable medium of Claim 20 amount to no more than mere instruction to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such, Claims 1 and 20 are not patent eligible.
Dependent Claims 7, 15, 16, and 18 solely narrow the abstract idea and do not recite any additional elements. Claim 7 narrows the abstract idea by specifying the propensity scores comprise real numbers greater than zero and less than or equal to one. Claim 15 recites dynamically adjusting recruitment at periodic time intervals. Claim 16 specifies the periodic time interval is at least monthly. Claim 18 specifies the threshold value is at least 0.10.
Dependent Claims 2, 3, 12, 13, and 21 recite new additional elements with new limitations. Claim 2 recites a propensity score model. Claim 3 recites a logistic regression model, a machine learning based propensity score model, a probit model, neural networks, support vector machines, decision trees, or meta-classifiers. Claim 12 recites the use of a clinical database. Claim 13 recites the use of a Patient Reported Outcomes (PROs) database. Claim 21 recites the use of a processor.
Dependent Claims 4-6, 8-11, 14, 17, and 19 recite previously cited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 4 narrows the additional elements of the propensity score model of Claim by specifying the model matches ECA participants to RCT participants, Claim 5 narrows the additional elements of the propensity score model of Claim by specifying the model uses the propensity scores to weight ECA and RCT participants, Claim 6 narrows the additional elements of the propensity score model, RCT participant database, and ECA participant database by specifying the model by specifying the model weights the propensity score downward or upward based on similarity of the records in the database, Claim 8 narrows the additional elements of the ECA and RCT participant databases by specifying the patient data in the databases being weighted in accordance with an overlap weighting methodology, Claim 9 narrows the additional elements of the ECA and RCT participant databases by specifying the patient data in the databases are weighted in accordance with an inverse probability or treatment weighing (IPTW) methodology, Claim 11 narrows the additional element of the ECA candidate database by specifying it comprises both EHR and non-EHR data, Claim 14 narrows the ECA candidate database, ECA participant dataset, and RCT participant database by specifying adjusting recruitment when an imbalance between the data in the databases is identified, Claim 17 narrows the ECA participant dataset and RCT participant database by specifying calculating an absolute standardized mean difference between at least one feature variable of the RCT and ECA participant records and identifying imbalances, Claim 19 narrows the ECA candidate database, ECA participant dataset, and RCT participant database by specifying contacting at least one site and adding one or more ECA candidate records to the databases.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide a conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 10-12, 14 and 20-21 are rejected under 35 USC § 103 as being unpatentable over Li et al. (Li, J., et al. "A matching design for augmenting a randomized clinical trial with external control." arXiv preprint arXiv:2203.10128 (March 2022).) in view of Kalathil et al. (WO 2016/133708 A1), further in view of Ohara et al. (US 20170132538 A1) and Westreich et al. (Westreich, Daniel et al. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of clinical epidemiology vol. 63,8 (2010) (Year: 2010)).
Regarding Claim 1, Li discloses the following:
A method of designing a hybrid clinical trial including an external control arm (ECA) study located in at least one site, to support a randomized clinical trial (RCT) study for a treatment of a medical condition, the method comprising: administering the treatment to at least some of a plurality of RCT participants, wherein each RCT participant is an individual person; (Li discloses a method that improves on the work of Yuan et al 28 by matching the entire concurrent RCT rather than only a subset of the active treatment arm for the following rationales. Randomly subsampling matches from active treatment group for concurrent control and then obtaining matches from external control for the rest of the treated (unmatched treated) is equivalent to randomly selecting a subset from treatment arm directly used for obtaining matches from external control (p. 2, ¶ 0002). The real clinical trial consists of patients who completed the 52-week measurement of HbA1c (p. 5, ¶ 0003).)
comparing a set of values corresponding to a second set of feature variables obtained from the ECA participant records …to a set of values corresponding to the second set of feature variables obtained from the RCT participant records … (Li discloses the nature of RCT (i.e., randomization) guarantees that the concurrent control and active treatment arm are comparable with respect to both measured and unmeasured covariates (p. 2, ¶ 0001). By using our proposed approach, a common set of control subjects is used for comparison, which is more similar to what a typical RCT does (p. 2, ¶ 0002.))
… wherein each participant record corresponds to an … participant and comprises information about administering the treatment to the … participant…wherein each candidate record corresponds to an individual person receiving a standard of care (SOC) treatment for the condition,… (Li discloses studying the casual effect of a treatment compared with a placebo (p. 1, para. 0001). The real clinical trial consists of patients who completed the 52-week measurement of HbA1c, a metric used to evaluate the patients’ level of blood glucose (p. 5, para. 0003).)
wherein the RCT feature is compared to a pooled set of ECA sites so that balance is achieved between the RCT and the set of ECA sites pooled together; (Li discloses an improved matching design that matches the entire RCT rather than a randomly selected subset. As such, the matched external control subjects are more comparable to the RCT population. Using a weighted estimator with a fixed weight, our method can be easily applied to an RCT with multiple treatment arms and every active treatment arm in the RCT is compared with a common control group (p. 6, ¶ 0005).)
Li does not disclose the following limitations met by Kalathil:
wherein each candidate record corresponds to an ECA candidate and comprises information … to the ECA candidate; (Kalathil teaches a method of designing a clinical trial involves aggregating patient medical records of multiple patients to establish a comprehensive database of the patient medical records of the multiple patients. The medical record of each patient in the database includes information describing the characteristics and conditions of each patient (p. 2, ¶ 0005).)
and dynamically adjusting recruitment into at least one ECA participant database at the at least one site that is recruiting participants into the ECA study… (Kalathil teaches excessive or insufficient numbers of prospective participants are avoided without compromising the clinical trial, by dynamically adjusting the clinical trial criteria. Dynamically adjusting the clinical trial criteria relative to the specific etiological conditions of the prospective participants assures an adequate number of suitable participants from a massive pool of prospective participants (p. 2, ¶ 0003).)
…a plurality of candidate records in an … database located in the at least one site of a plurality of … sites (Kalathil teaches the entity which aggregates the patient medical records of multiple patients in the database, referred to herein as an "Aggregator." (p. 9, ¶ 0004). Adequate patient enrollment and participation in different design stages of a clinical trial is facilitated and scaled by dynamically adjusting clinical trial criteria relative to characteristics and conditions of massive numbers of patients whose medical records have been aggregated in databases (p. 1, ¶ 0001).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the patient’s in the trial having an information database and adjusting the clinical trial recruitment based on the prospective patient’s data as taught by Kalathil. This modification would create a method which can run the most accurate clinical trial and therefore properly test new drugs or medical devices (see Kalathil, p. 5, ¶ 0003-4).
Li and Kalathil do not teach the following limitations met by Ohara:
by calculating a Mahalanobis distance value based on: a point comprising a set of values corresponding to a first plurality of feature variables obtained from the candidate record corresponding to the at least one outlier ECA candidate, (Ohara teaches the model creating device may include an outlier remover configured to remove outliers from operating data … [0009]. FIG. 6 is a drawing illustrating an example of the χ-square distribution created by the outlier remover 131. In FIG. 6, the horizontal axis shows the Mahalanobis distance D, and the vertical axis shows the probability density function P [0057, Formula 1].)
and a distribution comprising a plurality of points, wherein each point comprises a set of values corresponding to the first plurality of feature variables obtained from each of a plurality of …. (Ohara teaches the outlier remover 131 calculates a probability density function P, based on the formula 2 described below, by using the Mahalanobis distance D, and creates a χ-square distribution [0056].)
excluding at least one outlier ECA candidate from at least one ECA participant database at the at least one site based on whether the Mahalanobis distance value meets a specified criteria; (Ohara teaches the outlier remover 131 reads the operating data 181 of the plant 60 out of the storage 160, and removes outliers from the operating data 181 by using Mahalanobis distance. Specifically, the outlier remover 131 converts multivariate operating data X into the Mahalanobis distance D, based on the formula 1 described below, by using an average value μ thereof and a variance-covariance matrix V [0055]. The outlier removed…removes the data (outliers) exceeding the threshold value TH0 from the operating data 181 [0055]. The threshold is interpretted as the specified criteria.)
detecting at least one outlier …(Ohara teaches the outlier remover 131 reads the operating data 181 of the plant 60 out of the storage 160, and removes outliers from the operating data 181 by using Mahalanobis distance [0055].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the use of the Mahalanobis in order to determine and remove outliers in the data as taught by Ohara. This modification would create a method which can removal abnormal data points and therefore run the most accurate clinical trial (see Ohara, ¶ 0054).
Li, Kalathil, and Ohara do not teach the following limitations met by Westreich:
and matching propensity scores for exposed and unexposed individuals in the RCT participant databases and the set of ECA sites to obtain an unbiased estimate of an average causal effect of the treatment on the medical condition (Westreich teaches patterns, and in particular by achieving balance on confounders by propensity score. The key assumption made is that, given an exposed individual and an unexposed individual with the same (or nearly the same) propensity score, treatment assignment for these two individuals is independent of all confounding factors, and so the two observations can serve as counterfactuals for the purpose of causal inference. Under the key assumption of no unobserved or unmeasured confounding, matching exposed and unexposed individuals in a cohort will allow the data analyst to obtain an unbiased estimate of the average causal effect of the treatment on the outcome while maintaining good precision compared to more traditional maximum likelihood regression analysis (p. 2, ¶ 0002).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the matching of propensity scores for exposed and unexposed individuals to obtain an estimate of the causal effect of treatment as taught by Westreich. This modification would create a method which can control confounding bias in the assessment of the average effect of a treatment (see Westreich, 2, ¶ 0001-2).
Regarding Claim 20, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Li and Kalathil do not teach the following limitation met by Ohara:
A non-transitory computer readable medium comprising processor- executable instructions (Ohara teaches an aspect of the present invention is to provide a plant model creating device, a plant model creating method, and a non-transitory computer readable storage medium [0028].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the use of a non-transitory computer-readable medium as taught by Ohara. This modification would create a machine which can comprehensively implement the instructions that it is provided (see Ohara, ¶ 0028).
Regarding Claim 2, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 1 above. Li further discloses:
further comprising fitting a propensity score model by calculating propensity scores using RCT and ECA participant records to adjust for one or more measured confounders. (Li discloses for each subject drawn from the super-population, the probability of being selected to the RCT or EC is determined by the following logistic propensity score model (p. 5, ¶ 0004). For each simulated data set, we first estimate the propensity score for being selected to the RCT or EC using a logistic regression which regresses data source indicator D on the observed baseline covariates X. We then apply the optimal matching on the estimated propensity score to obtain a matched set from the EC data for the entire RCT without replacement (p. 6, ¶ 0001). All these methods intend to remove confounding bias from the observed covariates (p. 2, ¶ 0001).)
Regarding Claim 3, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 2 above. Li further discloses:
wherein the propensity score model is estimated using one or more of a logistic regression model, a machine learning based propensity score model, a probit model, neural networks, support vector machines, decision trees, or meta-classifiers. (Li discloses for each simulated data set, we first estimate the propensity score for being selected to the RCT or EC using a logistic regression (p. 6, ¶ 0001).)
Regarding Claim 4, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 3 above. Li further discloses:
wherein propensity scores estimated using the propensity score model are used to match ECA participants to RCT participants based on the one or more measured confounders. (Li discloses for each simulated data set, we first estimate the propensity score for being selected to the RCT or EC using a logistic regression (p. 6, ¶ 0001).)
Regarding Claim 5, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 3 above. Li further discloses:
wherein propensity scores estimated using the propensity score model are used to weight ECA and RCT participants based on one or more measured confounders. (Li discloses estimator can be formed by a weighted average of the concurrent and matched external controls, whose variance can be estimated by simply assuming the data after matching (without replacement) are approximately independent 29, or by bootstrapping the matched pairs or sets 30. Moreover, an RCT is often associated with multiple treatment arms…(p. 2, ¶ 0002).)
Regarding Claim 10, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 1 above. Li, Ohara, and Westreich do not teach the following limitation met by Kalathil:
wherein the at least one ECA candidate database comprises an electronic health records (EHR) database at a site. (Kalathil teaches the characteristics and conditions of a first group of patients in the database are established by collecting a basic electronic healthcare record (EHR) of a patient (p. 9, ¶ 0004).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the candidate data including electronic health data in a database as taught by Kalathil. This modification would create a method which can recruit and add patients to a trial that have the characteristics and conditions for the trial (see Kalathil, p. 5, ¶ 0005).
Regarding Claim 11, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 1 above. Li, Ohara, and Westreich do not teach the following limitation met by Kalathil:
wherein the at least one ECA candidate database comprises both EHR data and non-EHR data. (Kalathil teaches suitable patients are identified from the database who have characteristics and health conditions which match the selected clinical trial criteria, and the clinical trial is designed and conducted by reference to the identified patients (p. 9, para. 0006). Participants may be required to have specific characteristics of age, gender, ethnicity, allergies, pre-existing and other related medical conditions, and the like (p.5, ¶ 0005).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the candidate data including both electronic health data and non-electronic health data in a database as taught by Kalathil. This modification would create a method which can recruit and add patients to a trial that have the characteristics and conditions for the trial (see Kalathil, p. 5, ¶ 0005).
Regarding Claim 12, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 11 above. Li, Ohara, and Westreich do not teach the following limitation met by Kalathil:
wherein the non-EHR data comprises a clinical database at a site. (Kalathil teaches participants may be required to have specific characteristics of age, gender, ethnicity, allergies, pre-existing and other related medical conditions, and the like (p.5, ¶ 0005).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the candidate data including non-electronic health data such as clinical data in a database as taught by Kalathil. This modification would create a method which can recruit and add patients to a trial that have the characteristics and conditions for the trial (see Kalathil, p. 5, ¶ 0005).
Regarding Claim 14, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 1 above. Li further discloses:
when an imbalance is identified in the comparison of the set of values corresponding to the second set of feature variables obtained from the ECA participant records in the at least one ECA participant database and the set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database, (Li discloses matching the entire RCT can be applied to an RCT with multiple arms, where every AT arm of the RCT is compared with the same set of control subjects. A weighted estimator …can be constructed to resemble the balanced allocation design (p. 4, ¶ 0003).)
Li and Ohara do not teach the following limitations met by Kalathil:
wherein dynamically adjusting recruitment from at least one site recruiting participants into the ECA comprises adding one or more ECA candidate records from at least one site-specific ECA candidate database to at least one ECA participant database (Kalathil teaches determining that the number of first identified patients is inadequate to continue designing the clinical trial, changing at least one of the characteristics or conditions of the clinical trial criteria to create adjusted or reformulated clinical trial criteria, and identifying a second group of suitable patients from the database who have characteristics and conditions which match the reformed clinical trial criteria (p. 10, para. 0002). This is interpretted as determining an imbalance and changing the participants in the trial accordingly.)
wherein the imbalance is corrected to within a balancing range when the one or more ECA candidate records are added into the at least one ECA participant database. (Kalathil teaches interacting with the full medical records of massive numbers of patients allows the clinical trial criteria to be adjusted or reformulated on a dynamic basis while designing the clinical trial. An adequate number of prospective participants is straightforwardly estimated at each stage of designing the clinical trial (p. 8. ¶ 0010).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study, comparing the control arm patient data to the RCT data, and determining an imbalance as disclosed by Li to incorporate dynamically adjusting recruitment to correct imbalances as taught by Kalathil. This modification would create a method which can recruit and add patients to a trial that have the characteristics and conditions for the trial (see Kalathil, p. 5, ¶ 0005).
Regarding Claim 21, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 20 above. Li, Ohara, and Westreich do not teach the following limitations met by Ohara:
A computer system comprising one or more processors coupled to the non- transitory computer readable medium of claim 20. (Ohara teaches the operating data obtainer 120, the operation characteristics analyzer 130, the model creator 140, and the energy flow diagram creator 150 are implemented by a processor, such as CPU (Central Processing Unit), executing a program stored in the storage 160 [0042].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the use of a computer system with processors as taught by Ohara. This modification would create a machine which can comprehensively implement the instructions that it is provided (see Ohara, ¶ 0028).
Claim 6 is rejected under 35 USC § 103 as being unpatentable over Li, Kalathil, Ohara, and Westreich in view of Lee et al. (LEE, B. et al., "Improving propensity score weighting using machine learning," Stat Med., 2010, Vol. 29, No. 3, pp. 337-346.).
Regarding Claim 6, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 5 above. Li, Kalathil, Ohara, and Westreich do not teach the limitations met by Lee:
wherein the estimated propensity score on at least one ECA participant record is weighted downward if the propensity score model indicates that it is relatively dissimilar to one or more RCT participant records in the RCT participant data and the estimated propensity score on the at least one RCT participant record is weighted upward if the propensity score model indicates that it is relatively dissimilar to one or more ECA participant records in the ECA participant database. (Lee teaches we assigned treated persons a weight of 1 while untreated persons are assigned a weight of pi /(1−pi ) [12,13,15,29]. Thus, persons in the comparison group who are more similar to those in the treatment group are given greater weight and those more dissimilar are down weighted. If the propensity scores are properly estimated, then the weighted covariate distributions between treatment groups should be similar and the average treatment effect can be estimated as the difference of weighted means (p. 4, para. 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate down weighting candidates that are less similar to the treatment group as taught by Lee. This modification would create a method which can weight the data to account for unequal probabilities in a study sample (see Lee, p. 4, ¶ 0002).
Claims 7-9 are rejected under 35 USC § 103 as being unpatentable over Li, Kalathil, Ohara, and Westreich in view of Lee, further in view of Desai et al. (DESAI, R. et al., “Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners," BMJ 2019, Vol. 367, No. 15657, 10 pages.).
Regarding Claim 7, Li, Kalathil, Ohara, Westreich, and Lee teach the limitations as shown in the rejection of Claim 6 above. Li, Kalathil, Ohara, Westreich, and Lee do not teach the limitations met by Desai:
wherein the propensity scores comprise real numbers greater than or equal to zero and less than or equal to 1. (Desai teaches the propensity score is a number between 0 and 1 as Fig. 3 displays a propensity score graph with the propensity score range being 0 to 1.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the propensity score being a real number as taught by Desai. This modification would create a method which can provide the probability via the propensity score (see Desai, p. 1, ¶ 0002).
Regarding Claim 8, Li, Kalathil, Ohara, Westreich, and Lee teach the limitations as shown in the rejection of Claim 7 above. Li, Kalathil, Ohara, Westreich, and Lee do not teach the limitations met by Desai:
wherein patient data in the ECA and RCT participant databases is weighted by the propensity score in accordance with an overlap weighting methodology. (Desai teaches Overlap weights- This method involves weighting patients based on the predicted probability of receiving the opposite treatment. Similar to matching weights, extreme weights are impossible as weights are bound between 0 and 1 by design and, therefore, no truncation is necessary (p. 7, para. 0002, see Fig. 1-3).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the data being weighted by the propensity score using an overlap weighting methodology as taught by Desai. This modification would create a method which can properly weigh variables and transparently report the balance achieved between treatment and reference populations (see Desai, p. 1, ¶ 0003).
Regarding Claim 9, Li, Kalathil, Ohara, Westreich, and Lee teach the limitations as shown in the rejection of Claim 7 above. Li, Kalathil, Ohara, Westreich, and Lee do not teach the limitations met by Desai:
wherein patient data in the ECA and RCT participant databases are weighted by the propensity score in accordance with an inverse-probability of treatment weighing (IPTW) methodology. (Desai teaches inverse probability treatment weighting (IPTW)-This method involves weighting by the inverse probability of receiving the study treatment actually received (1/propensity score for the treated group and 1/(1-propensity score) for the reference group) (p. 5, para. 0005, see also Fig. 1-3).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the data being weighted by the propensity score using an inverse-probability of treatment weighting (IPTW) methodology as taught by Desai. This modification would create a method which can properly weigh variables and transparently report the balance achieved between treatment and reference populations (see Desai, p. 1, ¶ 0003).
Claim 13 is rejected under 35 USC § 103 as being unpatentable over Li, Kalathil, Ohara, and Westreich in view of Sharma et al. (US 20220301670 A1).
Regarding Claim 13, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 11 above, Li, Kalathil, Ohara, and Westreich do not teach the following limitations met by Sharma:
wherein the … data comprises a Patient Reported Outcomes (PROs) database. (Sharma teaches the pathology reports can include…patient reported outcomes database [0004].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the data comprising a patient reported outcomes database taught by Sharma. This modification would create a method which can utilize large portions of data to optimally analyze the patients (see Sharma, ¶ 0003).
Claims 15-16 are rejected under 35 USC § 103 as being unpatentable over Li, Kalathil, Ohara, and Westreich in view of Francois et al. (US 20160110523 A1).
Regarding Claim 15, Li, Kalathil, Ohara, and Westreich teach the limitations as shown in the rejection of Claim 1 above. Li, Ohara, and Westreich do not teach the following limitations met by Kalathil:
wherein the step of dynamically adjusting recruitment from at least one site recruiting participants… (Kalathil teaches the present invention…obtains the medical records of a massive number of patients in compliance with patient privacy and confidentiality laws and regulations and which effectively adjusts or reformulates clinical trial criteria to identify suitable participants when designing a clinical trial (p. 5, para. 0002). The number of patients and their medical records in the database are continuously changed or updated as the characteristics and conditions of the patients continuously change and patients continuously receive Healthcare. When the number of identified second patients is inadequate to continue designing the clinical trial, the procedure offers the opportunity to wait for the patient medical records to update. Thereafter, matching the trial criteria with the updated database perm it’s the identification of a different member of suitable patients who have characteristics and health conditions which match the clinical trial criteria (p. 10, ¶ 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate dynamically adjusting recruitment variables as taught by Kalathil. This modification would create a method which can recruit and add patients to a trial that have the characteristics and conditions for the trial (see Kalathil, p. 5, ¶ 0005).
Li, Kalathil, Ohara, and Westreich do not teach the following limitations met by Francois:
performed at periodic time intervals for a time duration of the hybrid clinical trial. (Francois teaches at least some health information entered by a user may remain stored on a user's computer for a period of time prior to being transmitted to the EMR system. Such transmission may occur in response to a request initiated by the EMR system, which may occur automatically, e.g., at predetermined time intervals [0077].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for designing a hybrid clinical trial which uses both an external control arm and a randomized clinical trial study and comparing the control arm patient data to the RCT data as disclosed by Li to incorporate the dynamic adjustments of the recruitment process as taught by Kalathil, and further to incorporate adjustments occurring at period time intervals as taught by Francois. This modification would create a method which uses data that is as accurate and comprehensive as possible